Computerized tests generate rich behavioral data that are rarely used in educational and
psychological assessments. We propose to analyze detailed response behaviors—including
response times, cursor trajectories, and distractor choices—to distinguish between three distinct
response processes: typical test-taking, carelessness potentially stemming from
disengagement, and patterns characteristic of ADHD-related executive dysfunction (e.g.,
heightened intra-individual response variability). Utilizing the Invenio cognitive screening tool for
children (aged 6-15) across core C-H-C cognitive domains, and leveraging advanced
psychometric techniques and machine learning, we will identify robust behavioral indicators.
These will inform the development and cross-validation of an interpretable predictive model,
validated against external criteria: child self-reported test-taking experience and motivation, and
parent-reported ADHD symptoms. Our findings have the potential to facilitate timely intervention
and enhance test score interpretation in psychological and educational assessment.

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